Real-Time Automatic Colour Calibration for NAO Humanoids

A challenge in NAO soccer robots is colour calibration. Good colour calibration can result in robust and accurate self-localization of the robot. Currently manual calibration is the only solution, which is used. In this paper, we are proposing an automatic real-time, accurate YUV colour space based colour calibration technique. In order to define average values for the desired colour classes namely orange, white, green and purple, a specified set of frames from the NAO camera are analysed. These average values are corrected by luminance analysis of a new frame and are passed to the K-means clustering algorithm as a set of initial means. In addition to these four values, a set of initial means of the K-means algorithm contains 16 values that are calculated in the following manner: the frame being processed is divided into 4 by 4 grids and the average value from every grid serves as an initial mean for K-means clustering. Consequently, colours of a similar type are combined into clusters. The final step of the proposed technique is cluster classification in which the average values of the desired colour classes are corrected by luminance analysis. As NAO cameras provide video streams in YUV format and the proposed algorithm uses this format there is no need for additional computational steps for conversation between the colour spaces. As a result, computational process is reduced compared to current techniques

Melioration of color calibration, goal detection and self-localization systems of NAO humanoid robots

Selle lõputöö teemaks on autonoomsete robotite jalgpalli tarkvara arendamine.Vaatluse all on teemad nagu värvide kalibreerimine, objetkituvastus ja lokaliseerimine. Uus YUV värviruumi põhine automaatne värvide kalibreerimine on pakutud. Esitatakse detailne kirjeldus automaatse värvide kalibreerimise algoritmi implemenmtreerimisest koos visuaalsete näidetega, mis illustreerivad algoritmi toimimist. Samuti räägitakse täpsemalt muutustest, mis on implementeeritud väravate tuvastamise moodulis ja põhjustest nende muudatuste taga, andes hea ülevaate objekti tuvastamise algoritmi loogikast. Kirjeldatakse hetkel kasutatavat lokaliseerimissüsteemi ja pakutakse välja ning seletatakse lokaliseerimissüsteem parandamise tehnikat.In this thesis, work regarding to autonomous robot soccer software development is presented. The work covers color calibration, object detection and robot localization topics. Novel YUV color space based method for the automation of color calibration is proposed. Detailed description of automatic color calibration technique implementation is provided along with the visual results illustrating performance of the method. Changes implemented to the goal detection module and motivation behind them are described in detail, providing good overview of the logic of the object recognition algorithm. Utilised localisation system is also described and, finally, the localization system enhancement technique is proposed and explained

Learning Motion Skills for a Humanoid Robot

This thesis investigates the learning of motion skills for humanoid robots. As groundwork, a humanoid robot with integrated fall management was developed as an experimental platform. Then, two different approaches for creating motion skills were investigated. First, one that is based on Cartesian quintic splines with optimized parameters. Second, a reinforcement learning-based approach that utilizes the first approach as a reference motion to guide the learning. Both approaches were tested on the developed robot and on further simulated robots to show their generalization. A special focus was set on the locomotion skill, but a standing-up and kick skill are also discussed. Diese Dissertation beschäftigt sich mit dem Lernen von Bewegungsfähigkeiten für humanoide Roboter. Als Grundlage wurde zunächst ein humanoider Roboter mit integriertem Fall Management entwickelt, welcher als Experimentalplatform dient. Dann wurden zwei verschiedene Ansätze für die Erstellung von Bewegungsfähigkeiten untersucht. Zu erst einer der kartesische quintische Splines mit optimierten Parametern nutzt. Danach wurde ein Ansatz basierend auf bestärkendem Lernen untersucht, welcher den ersten Ansatz als Referenzbewegung benutzt. Beide Ansätze wurden sowohl auf der entwickelten Roboterplatform, als auch auf weiteren simulierten Robotern getestet um die Generalisierbarkeit zu zeigen. Ein besonderer Fokus wurde auf die Fähigkeit des Gehens gelegt, aber auch Aufsteh- und Schussfähigkeiten werden diskutiert

Bowdoin Orient v.137, no.1-25 (2007-2008)

https://digitalcommons.bowdoin.edu/bowdoinorient-2000s/1008/thumbnail.jp